Freight Transport Demand Research Articles

Corona crisis disruptions and consequences for demand for freight transport: A screening of the effects for the largest logistics service providers in Norway

AbstractThe present article investigates corona crisis disruptions and consequences for freight transport demand, using shipment data from major logistic service providers in Norway, as well as interviews. We investigate how Covid-19 affected demand for freight transport and transport volumes in different segments, changes for parcel delivery, home-deliveries, which industries were most affected and discussed main issues for LSPs in maintaining robust transport chains. We find temporary reductions in B2B deliveries after the first Norwegian societal lockdown but large volume increases in B2C deliveries, which remained at higher levels and saw further waves of increases. Especially home deliveries and mailbox deliveries of parcels increased, particularly in relation with new infection waves. At an aggregate level, retail trade experienced by far the largest volume increases, followed by transport and storage. The latter can in part be due to increasing low-volume import (parcels) from abroad being attributed to e.g. warehouses or customs. At the other end of the scale, wholesale trade stands out with the largest tonnage decrease, followed by manufacturing.

Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion

The growth of urbanization, population, and economic activity has led to a substantial increase in freight transportation demand, exceeding the capacity of existing infrastructure and creating new challenges across various regions. This has resulted in significant traffic congestion, increased travel times, and higher operational costs for commercial vehicle fleets. Leveraging topological data, such as road networks and traffic patterns, can enable more efficient routing strategies to navigate around congested areas. This study presents a comprehensive approach to truck rerouting strategy by integrating spatial analysis, truck characteristics, traffic conditions, road geometry, and cost–benefit analysis to select alternative routes suitable for commercial vehicle fleets. Incorporating real-time traffic information and predictive analytics, commercial vehicle operators can optimize their routes, reduce fuel consumption, and improve overall delivery efficiency. Three case studies were presented to demonstrate the proposed diversion decision framework. Two scenarios were designed for each case study: a base scenario with no diversion and an optimized scenario with a diversion strategy. The travel times, fuel consumption, and economic impacts between the two scenarios were compared and quantified as a total annual saving of USD 52 million. This approach goes beyond selecting alternative routes and provides decision makers with measurable benefits that justify diversion strategies.

Reexamining exposure from truck emissions considering daily movement of individuals

With the growing demand for urban freight transportation, trucks emit a large amount of pollutants such as particulate matters and nitrogen oxides, increasingly affecting public health. This study establishes a modelized air dispersion structure to simulate pollutant concentration distribution. By integrating multiple data sources including mobile phone signals and satellite images, we reconstruct the daily trajectories of individuals and further incorporate simulated pollution concentrations in calculating dynamic and static exposure of individuals to truck emissions. Econometric models considering spatial dependence are developed to evaluate the influencing factors and elucidate the mechanisms of pollutant exposure. Results show factors including freight demand, road network, residential and employment locations, personal commuting distance, and population age structure matter in assessing truck emission exposure. As a result, a mixture of vehicular emission standards, urban traffic control, land planning, and industrial policies is proposed to reduce truck pollutant exposure and safeguard public health.

Optical Measurement System for Monitoring Railway Infrastructure—A Review

Rail infrastructure plays an important role in fulfilling the demand for freight and passenger transportation. Increases in traffic volume, heavier axles and vehicles, higher speeds, and increasing climate extremes all contribute to the constant strain on the infrastructure. Due to their major importance in the transportation of people and freight, they are subject to continuous condition monitoring. This is an essential requirement for the selective planning of maintenance tasks and ultimately for safe and reliable operation. Various measuring systems have been developed for this purpose. These must measure precisely, quickly, and robustly under difficult conditions. Whether installed from mobile or stationary platforms, they have to cope with a wide range of ambient temperatures and lighting conditions, harsh environmental influences, and varying degrees of reflection. Despite these circumstances, railway operators require precise measurement data, high data densities even at high traveling speeds, and a user-friendly presentation of the results. Photogrammetry, laser scanning, and fiber optics are light-based measurement methods that are used in this sector. They are able to record with high precision rail infrastructure such as overhead contact systems, clearance profiles, rail tracks, and much more. This article provides an overview of the established and modern optical sensing methods, as well as the use of artificial intelligence as an evaluation method, and highlights their advantages and disadvantages.

Gansu, Qinghai and Ningxia regions of the new western land and sea corridor Freight network optimization

The new western land and sea corridor makes use of a variety of transport modes to reach major ASEAN countries such as Singapore to the south, connects Southeast Asia and North America to the east, and connects Chongqing, Lanzhou and Xinjiang to the north with the China-European Union (CEU) liner train, which is a composite opening-up corridor in the western region for realising the regional linkage and international co-operation with ASEAN and other countries, and organically connecting with the "One Belt, One Road". In order to explore the regional freight network of Gan-Qing-Ning region of the new western land and sea corridor, we establish the model of node socio-economic attractiveness, topological charisma and comprehensive utility maximization, and analyze the index factors by using entropy weighting, Delphi and comprehensive evaluation methods through the Matlab Genetic Algorithm Toolbox to study the influence of node socio-economic attractiveness, topological charisma, node freight volume, logistics and transportation costs and construction costs on the utility of nodes of the new western land and sea corridor. The influence of the regional node utility of Gansu, Qinghai and Ningxia in the corridor. Analyze the problems and factors affecting the selection of nodes in the existing freight transport corridors in the three provinces. Completing the site selection of Gan-Qing-Ning regional node of the new western land and sea corridor and combining the import and export cargo volume of the three provinces to propose the construction of the South-Middle East three-lane freight corridor, and completing the optimization plan of the freight network. The results show that: scientific optimization and improvement of Gan-Qing-Ning regional freight network can promote the construction of freight network in the northwest region of the new western land and sea corridor, to meet the demand for freight transportation in the corridor, and to promote the development of the corridor industry and economic growth. Thus, it provides reference for the development of Gansu, Qinghai and Ningxia region and freight network optimization.

An Analysis of the Development Factors of Rail Freight Transport in Thailand: A Structural Equation Modeling Approach

The railway infrastructure projects in Thailand aim to shift transportation from roads to railways. This is crucial for transporting goods in emerging economies and increasing the demand for rail freight transport. However, several dynamic uncertainties hinder sustainable rail freight transport in Thailand. This study aims to identify the key factors and validate their effects on the success of the modal shift from roads to railways in Thailand. A total of 200 participants filled out a questionnaire delivered online and via postal service. The key factors were categorized into the following categories: the rail freight transport system, demand, and development factors in Thailand. The inter-relationship and connection of these factors were analyzed using SEM (structural equation modeling). The SEM results showed that all causal factors in the model had a positive influence on rail freight development in Thailand, which explained 98.3% of the variance in the factors influencing development. This study’s findings underscore the influential significance of rail performance, rail infrastructure, the legal framework, pricing, mode choice, and technology on the expansion of rail freight transport in Thailand. The rail freight transport system, rail performance, rail infrastructure, and Thailand’s rail infrastructure development strategy were significant direct predictors of rail freight expansion. An expansion of the rail freight transport system also leads to rail freight demand. The results of this study have positive implications for the government, railway practitioners, and policymakers to prioritize their focus on achieving rail freight transport as the national target.

Study on the Influencing Factors of Regional Logistics on Regional Economy in Shanxi Province

With the acceleration of the pace of Belt and Road construction and the strategic deployment of the high-quality development of China's logistics service industry, the logistics industry has occupied an increasingly important position in the whole national economy and even the world economy. Shanxi Province is rich in coal resources and has a large demand for logistics and freight transportation. Therefore, the economic growth of Shanxi Province has put forward higher requirements for the development and progress of the logistics industry. In this situation and background, the development of regional logistics will be of great help to the progress and growth of the overall industrial economy in Shanxi Province. This paper takes the theory of modern logistics, regional economic growth and regional coordinated development as guidance, and discusses the coordinated development of logistics industry and economic sustainable development in Shanxi Province. Firstly, a series of theoretical analysis on the coordinated development of modern regional logistics, regional economy and the correlation between regional logistics and regional economy with the aid of data analysis software stata and the research results are summarized and summarized, trying to make some suggestions for the sustainable development of logistics and economy in Shanxi Province.

Uncertainties in the technological pathway towards low-carbon freight transport under carbon neutral target in China

The freight transport sector is a critical sector for achieving carbon peak and carbon neutrality targets. However, there are many uncertainties regarding the e-commerce development, promotion speed of low-carbon measures, and freight transport technologies. Existing research lacks considering the multiple uncertainties on the low-carbon technological pathway for freight transport. Consequently, a bottom-up National Energy Technology-Transport (NET-Transport) model is developed based on the principle of cost minimization for optimizing the low-carbon transition pathway of the freight transport. The study comprehensively evaluates the sustainable technological pathway, energy saving and carbon emission reduction of various measures, including shifting to low-carbon transport modes, improving transport energy efficiency and promoting clean energy. Results show that the freight transport demand will reach 24.1–60.5 trillion ton-kilometers by 2060. CO2 emission would peak at 0.8–1.0 billion tons in 2030–2041 if the breakthrough technologies are not developed on a large scale. Carbon neutrality in the freight sector is difficult to achieve by relying on the low-carbon transition of the energy system alone, with 57–555 million tons of remaining CO2 emissions in 2060. The advanced technologies have significant effects on energy saving and emission reduction. The research, development, and application of hydrogen-fueled heavy trucks should be accelerated.

Determination of demand for freight rail transportation. The relationship between the quality of transport services and demand

In the modern world, rail transport plays a huge role in ensuring the economic and social stability of the state. Due to its reliability, accessibility and safety, rail transport is able to meet the needs of a wide variety of population groups and sectors of the economy. One of the important aspects of the functioning of railway transport is its ability to provide efficient and high-quality cargo transportation services.In this regard, the purpose of this study is to identify the relationship between the demand for freight transportation and the level of service in the context of railway infrastructure. We rely on the non-price elasticity method to determine more precisely how changes in the quality of service can the demand for freight rail transportation.To achieve this goal, a number of tasks have been analyzed, such as analyzing the current state of railway transport, studying the possibilities of its growth and development, determining the current needs of the market and assessing the compliance of the services provided with these needs. In addition, the system of forecasting the demand for rail freight, based on the analysis of financial, economic and quantitative indicators, is considered. The first determine the capital costs of upgrading the current state of the infrastructure, focused on the repair and renewal of fixed assets. The latter determine the volume values of the cargo turnover and the average weight of the train. It is also necessary to develop a step-by-step methodology for determining the coefficient of non-price elasticity, to illustrate the dependence of demand for transportation on the level of quality of transport services.All this made it possible to determine how effective and expedient the existing cargo transportation service system is.The choice of this topic is due to the increasing need to increase the stability and reliability of railway transport, increase its accessibility and safety, as well as improve the quality of services provided. Thus, the identification of the relationship between the demand for rail freight and their quality based on the methodology of non-price elasticity contributes to the optimization of railway transport, improving its competitiveness, and stimulates the economic growth of the country.

Forecasting Ro-Ro Freight Transportation Demand at Samsun Port: A Hybrid Method Approach

ABSTRACT Türkiye's extensive coastline and geopolitics position necessitates the importance of Ro-Ro transportation with neighbouring countries. Türkiye's rapidly growing Ro-Ro transportation significantly contributes to imports and exports, which is of great importance to the national economy. Samsun Port is one of the most active ports in Türkiye's Ro-Ro transportation sector, operating in the Black Sea region. This study examined Ro-Ro transportation at Samsun Port, and future cargo forecasting was conducted. For this purpose, artificial neural networks and time series analysis methods were combined. Input variables used in the study included the number of Ro-Ro ships arriving at the port between 2009 and 2021, population figures, a specialized CPI indicator (fresh fruits and vegetables), and export values. The output variable was the amount of cargo carried by RoRo ships. According to the results obtained, it was observed that Samsun Port would have sufficient capacity for Ro-Ro transportation in the next 27 months in terms of wharf, port area, and operational space. Keywords: Samsun Port, Ro-Ro transportation, Port capacity, Forecasting

Method for calculating the supply of transport and logistics services depending on the demand for freight transportation

The article examines the problems that arise in railway freight transportation when determining the optimal size of rolling stock fleet under seasonal fluctuations in the volume of transportation and ensuring a certain reserve of vehicles. In order to optimize the number of vehicles in the fleet, it is necessary to find a balance between undesirable expenses for maintaining wagons in periods of falling demand for them (surplus supply of transport and logistics services) and unearned income from their lack (deficit) in periods of maximum growth of transportation volumes. The methodology and example of calculating the fleet of freight wagons, the efficiency of their use and determining the optimal number of rolling stock and the need for a certain reserve of vehicles are presented. It is noted that the railway transport of Ukraine was and continues to be one of the main types of transport in the transportation of goods and passengers. However, considering the long operating time of the rolling stock, more than 70% of semi-wagons and more than 80% of grain wagons require replacement or extension of their service life due to capital repairs. The role of railways is especially critical in the current period of russia's war against Ukraine, due to the lack of air transport, river and sea transport and the insufficient capacity of highways and border railway stations. Determining the required number and optimizing the number of vehicles in the fleet can be carried out according to the calculation methodology, which is based on the analytical determination of the minimum of the objective function of the total operating costs associated with both the deficit and the surplus of vehicles compared to the demand for transport and logistics services. It has been established that the application of this calculation method allows to optimize the inventory and working fleet of wagons taking into account the different cost per day of shortage and surplus of the fleet of wagons (supply of transport services compared to their demand). Compared to traditional methods of calculation, the proposed method saves operational costs for maintaining the fleet of wagons in the amount of 2-5%.

Forecasting the demand for railway freight transportation in Kazakhstan: A case study

Effective resource planning in railway freight transportation necessitates precise demand forecasting, shaped by a complex array of dynamic variables. In Kazakhstan, the National State-owned Railway Company (KTZ) underwent a transition from traditional expert-based forecasting methods to the application of mathematical models, notably the Autoregressive Integrated Moving Average (ARIMA) model. Employing historical data spanning from 2012 to 2016, this shift signifies a significant evolution in KTZ's approach to anticipating freight demand.The primary objective of this study is the empirical assessment of the efficacy of the ARIMA model in comparison to conventional qualitative forecasting techniques. Through the analysis of actual data from 2017, utilizing established metrics such as the Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE), this research substantiates the utility of time series analysis through ARIMA. The findings not only confirm the model's effectiveness but also emphasize its superiority in refining the precision of railway freight demand forecasts, particularly within the unique context of Kazakhstan.Beyond the validation of methodologies, this research serves as a precursor to advanced forecasting practices, offering the potential to redefine resource planning in the railway industry. By extending the predictive horizon to 2024, the manuscript aligns with contemporary standards, providing nuanced insights for operational and developmental considerations in Kazakhstan's railway freight sector. This expansion positions the study within the evolving landscape of the industry, ensuring a comprehensive and forward-looking contribution to efficient resource allocation and modernized planning practices.

A multi-objective optimization approach for disruption management in an intermodal freight transport network

The growing demand for freight transportation is leading to pay more attention to the management of intermodal freight transport network (IFTN). The optimization of IFTN management will have many benefits in terms of sustainability, reducing costs and pollution. Although many studies on the optimization of IFTN operations exist, the management of disruptions deserves a particular attention since disruptions may lead to a significant drop of performance in the entire network. In particular, seaport terminals are prone to disruptions because of their key role in connecting the land side and the sea-side operations. The availability and reliability of the entire network is directly affected by their disruptions. In this paper, a multi-objective linear programming model is developed to minimize congestion in an IFTN, which is originated from the occurrence of a disruption in a seaport terminal. The model aims to minimize cost and emissions, simultaneously. Computational results of a realistic case study, inspired by an Italian IFTN, shows the effectiveness of the developed model to improve the performance of the entire network.

ОСНОВЫ АНАЛИЗА СПРОСА НА ТРАНСПОРТИРОВКУ В ГОРОДСКОЙ ЧЕРТЕ

The purpose of the study is to determine the characteristics of the analysis of demand for the movement of urban goods. Such attention is established regarding the scale of the problem of urban freight transport, its impact on the quality of life of the population and the trends in the balance between urban and intercity movement of goods. The similarities and differences in the analytical approaches of passenger and freight transportation to forecasting demand for freight transportation are indicated. Basic definitions regarding the demand for urban freight transport are proposed

Enhancing Aerospace Industry Efficiency and Sustainability: Process Integration and Quality Management in the Context of Industry 4.0

This paper delves into the multifaceted domain of the aerospace industry, examining its evolution, current challenges, and imperative focus on quality management and process integration. The aerospace sector, driven by technological advancements and a burgeoning global demand for air travel and freight transport, necessitates a thorough analysis of its industrial fabric and operational intricacies. This research endeavors to analyze the dynamics of the aerospace industry, pinpoint its challenges, and propose an integrated approach to enhance efficiency, quality, and sustainability. The primary goals encompass understanding the evolving industry landscape, identifying critical challenges, and offering innovative solutions by amalgamating the principles of Industry 4.0 into quality management and processes within the aerospace sector. Through an in-depth exploration of various facets, this research underscores the pivotal role of efficient processes and integrated quality management in achieving sustainable growth and competitiveness in the aerospace industry. By aligning with the paradigm of Industry 4.0, organizations can optimize their operations and contribute to the industry’s advancement, delivering safer and more cost-effective aerospace products. The study adopts a multifaceted approach, incorporating an extensive literature review, a critical analysis of industry trends, the examination of quality management frameworks, and a thorough evaluation of the integration potential of Industry 4.0 technologies. The research also involves case studies and expert insights to validate the proposed approach. The investigation reveals that by leveraging Industry 4.0 technologies and embracing an integrated approach to quality management, the aerospace industry can significantly enhance operational efficiency, product quality, and overall sustainability. The seamless integration of processes and the implementation of advanced quality frameworks pave the way for a more competitive and future-ready aerospace industry, meeting the evolving demands of a globalized world.

Operational planning of international freight trains considering the dynamic demands and capacities of border ports

This paper presents a solution to address congestion at border ports that may cause significant delays and affect shipment delivery time for the China Railway Express trains. The proposed bi-objective 0–1 integer linear programming model takes into account the capacities of border ports and dynamic freight transportation demands at the network level over large time scales. A heuristic approach based on the Advanced NSGA-II (HANSGA-II) is proposed. Intensive numerical experiments using real network data were conducted to verify the proposed model and algorithm. Additionally, the sensitivity analysis proves that the proposed HANSGA-II is found to outperform the CPLEX solver in terms of solution quality, solution diversity and computational time, and can adapt to the expansion of future rail networks and the growth of demands. With the development of the consolidation mode, some cost parameters in the model can be adjusted to obtain more suitable solutions.

Modeling the Urban Freight-Transportation System Using the System Dynamics Approach

The dynamic and complex interactions between the urban freight-transportation system and population, economy, traffic flow, fuel consumption, and environmental pollution, make policymaking in this system one of the fundamental challenges of urban management. In this regard, a systemic approach in urban freight-transportation system modelling should be considered to solve the problems of the system. One of the main problems of this system is the mismatch between the freight-transportation capacity and the total freight-transportation demand. Considering the lack of sufficient studies in the field of macro and quantitative modeling of this system, the main goal of this article is to model the urban freight-transportation system in order to identify the factors affecting the urban freight-transportation demand and capacity. The main focus of the research is to develop quantitative scenarios which balance the freight-transportation capacity and freight-transportation demand. The urban freight-transportation system is modelled by the System Dynamics (SD) approach and their basic behaviors; as well as this the results of some policy-making scenarios are simulated. The model is validated by the real data of Shiraz. Five quantitative scenarios are designed with two approaches of managing the freight-transportation demand and freight-transportation-capacity sectors. The scenarios are based on four control variables, including the distribution coefficient, trip numbers, vehicle capacity, and vehicle numbers. The simulation results show that the total gap between freight-transportation capacity and freight-transportation demand will decrease by optimizing each of the control variables. However, the combined scenario is the most applicable policy in order to maintain the balance between freight-transportation capacity and demand. Generally, the proposed model can be used to design different quantitative scenarios in order to optimize the freight-transportation system’s performance. This study can also help policymakers to manage the urban freight-transportation system more efficiently.

Feasibility and optimization of freight-on-transit schemes for the sustainable operation of passengers and logistics

The integration of passengers and cargo flows is a promising strategy to reduce negative externalities and improve the operational and environmental performances of first-last mile transport and logistics. This is supported by the recent increase in just-in-time and fragmented deliveries, as well as technological advancements in transport and logistics. In this study, we investigate the financial and operational conditions under which an integrated system can be used instead of performing conventional independent passenger transport and freight deliveries to achieve the goal of reducing freight vehicle-km and -consequently-the associated environmental impact. We propose the generalization of a multi-commodity network design problem model to address customer and policymaker preferences simultaneously for the implementation of a freight-on-transit (FOT) system. We test the model in a cross-border area between Italy and Slovenia and propose a sensitivity analysis based on several scenarios that can simulate the preferences of stakeholders under different conditions. The results show that FOT can yield considerable benefits in terms of meeting freight transport demand if it is supported by adequate policies and technologies. Furthermore, political and stakeholder engagement plays a relevant role in the success of FOT initiatives, which are aimed at utilizing transportation services and include the reduction of transport-related externalities and, more generally, the environmental and social impacts of mobility.

Railway Freight Demand Forecasting Based on Multiple Factors: Grey Relational Analysis and Deep Autoencoder Neural Networks

The construction of high-speed rail lines in China has drastically improved the freight capacity of conventional railways. However, due to recent national energy policy adjustments, rail freight volumes, consisting mostly of coal, ore, and other minerals, have declined. As a result, the corresponding changes in the supply and demand of goods and transportation have led to a gradual transformation of the railway freight market from a seller’s market to a buyer’s market. It is important to carry out a systematic analysis and a precise forecast of the demand for rail freight transport. However, traditional time series forecasting models often lack precision during drastic fluctuations in demand, while deep learning-based forecasting models may lack interpretability. This study combines grey relational analysis (GRA) and deep neural networks (DNN) to offer a more interpretable approach to predicting rail freight demand. GRA is used to obtain explanatory variables associated with railway freight demand, which improves the intelligibility of the DNN prediction. However, the high-dimension predictor variable can make training on DNN challenging. Inspired by deep autoencoders (DAE), we add a layer of an encoder to the GRA-DNN model to compress and aggregate the high-dimension input. Case studies conducted on Chinese railway freight from 2000 to 2018 show that the proven GRA-DAE-NN model is precise and easy to interpret. Comparative experiments with conventional prediction models ARIMA, SVR, FC-LSTM, DNN, FNN, and GRNN further validate the performance of the GRA-DAE-NN model. The prediction accuracy of the GRA-DAE-NN model is 97.79%, higher than that of other models. Among the main explanatory variables, coal, oil, grain production, railway locomotives, and vehicles have a significant impact on the railway freight demand trend. The ablation experiment verified that GRA has a significant effect on the selection of explanatory variables and on improving the accuracy of predictions. The method proposed in this study not only accurately predicts railway freight demand but also helps railway transportation companies to better understand the key factors influencing demand changes.

Temporal stability of shipment size decisions related to choice of truck type

The choice of shipment size is a vital decision in logistics and has a strong indirect influence on freight transport demand, via the choice of mode and truck type choice. Through time, shipment sizes can change as a result of new decisions in the logistics process or due to conditions external to the supply chain. This study investigates the temporal stability of shipment size choices, relating these to the choice of truck types. It uses repeated cross-sectional data for the years 2015, 2017, and 2019 collected from cordon and business establishment surveys in Addis Ababa city, Ethiopia. The integrated choice and latent variables (ICLV) and latent growth (LG) models were used to assess the time-dependent patterns of choosing shipment sizes, both at the level of the entire freight system as well as the specific truck types. The model results reveal that shipment size decisions are temporally unstable where, in our case, shipment sizes exhibited a declining trend.